This disclosure relates generally to the field of image processing, and more particularly to an approach for detecting materials in a hyperspectral scene containing a plurality of pixels.
In many conventional image processing scenarios comprising hyperspectral imaging (HSI) systems, hyperspectral sensors collect data of an image from one spatial line and disperse the spectrum across a perpendicular direction of the focal plane of the optics receiving the image Thus a focal plane pixel measures the intensity of a given spot on the ground in a specific waveband. A complete HSI cube scene is formed by scanning this spatial line across the scene that is imaged. The complete HSI cube may be analyzed as a measurement of the spectrum, the intensity in many wavebands, for a spatial pixel. This spatial pixel represents a given spot on the ground in a cross-scan direction for one of the lines at a given time in the scan direction. Spectral information, however, cannot be exploited by simply looking at the HSI datacube. Instead, each spatial pixel spectra (in D dimensions or wavebands) is typically compared with reference spectra.
Matched Filter (MF) and Adaptive Cosine/Coherence Estimator (ACE) are common hyperspectral filters that are used along with Reed-Xiaoli (RX) anomaly detectors to detect the presence of materials in pixels including in a hyperspectral scene (scene pixels).
MF and ACE match an a priori spectrum or “reference spectrum” representative of a material to be detected to a measured scene pixel. MF and ACE are typically provided with a library or collection of reference spectrums, each representative of a different material. As such, MF and ACE compare each reference spectrum to the spectrum of each pixel. For each comparison, MF and ACE each provide a score (viz., MF score and ACE score) indicative of the likelihood that a respective reference spectrum matches the spectrum of a respective scene pixel. The MF score or ACE score is compared against a threshold. In which pixel is the material present is determined based on the comparison. Provided immediately below are the equations for calculating MF and ACE scores as well as an RX score.
            M      ⁢                          ⁢      F        =                                        (                          s              -                              μ                b                                      )                    T                ⁢                              ∑            b                          -              1                                ⁢                      (                          x              -                              μ                b                                      )                                                            (                          s              -                              μ                b                                      )                    T                ⁢                              ∑            b                          -              1                                ⁢                      (                          s              -                              μ                b                                      )                                          A      ⁢                          ⁢      C      ⁢                          ⁢      E        =                            [                                                    (                                  s                  -                                      μ                    b                                                  )                            T                        ⁢                                          ∑                b                                  -                  1                                            ⁢                              (                                  x                  -                                      μ                    b                                                  )                                              ]                2                                          (                          s              -                              μ                b                                      )                    T                ⁢                              ∑            b                          -              1                                ⁢                                    (                              s                -                                  μ                  b                                            )                        ⁢                                          (                                  x                  -                                      μ                    b                                                  )                            T                        ⁢                                          ∑                b                                  -                  1                                            ⁢                              (                                  x                  -                                      μ                    b                                                  )                                                                    R      ⁢                          ⁢      X        =                            (                      x            -                          μ              b                                )                T            ⁢                        ∑          b                      -            1                          ⁢                  (                      x            -                          μ              b                                )                    where:s=known target spectrum,x=pixel spectrum,μb=mean background, andΣb=covariance matrix